Titlebook: Complex Networks VIII; Proceedings of the 8 Bruno Gon?alves,Ronaldo Menezes,Vinko Zlatic Conference proceedings 2017 Springer International

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Affine Tasks for ,-Test-and-Set,work. It is known that social networks exhibit the (first-order) assortative mixing, i.e.?if two nodes are connected, they tend to have similar node degrees, suggesting that people tend to mix with those of comparable prominence. In this paper, we report the . assortative mixing in social networks.

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Lecture Notes in Computer Sciencerequencies on both the original and an ensemble of random networks keeping certain structural properties. The classical null model implies preserving the degree sequence. In this paper our focus is on a richer model that approximately fixes the frequency of subgraphs of size . to compute motifs of s

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Andrzej Pelc,Alexander A. Schwarzmannomposed of positive and negative edges. Although community detection in signed networks has been attempted by many researchers, studies for detecting detailed structures remain?to be done. In this paper, we extend modularity for signed networks, and propose a method for optimizing our modularity, wh

同位素

Christian Decker,Roger Wattenhoferanalyse such dataset is to study temporal motifs in link streams, i.e. sequences of links for which we can assume causality. In this article, we study the relationship between temporal motifs and communities, another important topic of complex networks. Through experiments on several real-world netw

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Anurag Agarwal,Vijay K. Garg,Vinit Ogaleer hand, these platforms have emphasized the dark side of information spreading, such as the diffusion of private facts and rumors in the society. Fortunately, in some cases, online social network users can set a level of privacy and decide to whom to show their information. However, they cannot con

障礙物

Giang Nguyen,Mathias Fischer,Thorsten Strufeifferent link prediction methods perform feature engineering to build different topological or nodal attribute based metrics measuring the similarity/proximity between non-connected actor pairs to deal with the inference of future associations among them. On the contrary, dynamic link prediction met